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<div class="title">cpc_segmentation.h</div>  </div>
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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/*</span></div>
<div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * Software License Agreement (BSD License)</span></div>
<div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment"> *  Point Cloud Library (PCL) - www.pointclouds.org</span></div>
<div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment"> *  Copyright (c) 2014-, Open Perception, Inc.</span></div>
<div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="comment"> *  All rights reserved.</span></div>
<div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="comment"> *  Redistribution and use in source and binary forms, with or without</span></div>
<div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="comment"> *  modification, are permitted provided that the following conditions</span></div>
<div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="comment"> *  are met:</span></div>
<div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="comment"> *   * Redistributions of source code must retain the above copyright</span></div>
<div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="comment"> *     notice, this list of conditions and the following disclaimer.</span></div>
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<div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;<span class="comment"> */</span></div>
<div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160; </div>
<div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;<span class="preprocessor">#ifndef PCL_SEGMENTATION_CPC_SEGMENTATION_H_</span></div>
<div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;<span class="preprocessor">#define PCL_SEGMENTATION_CPC_SEGMENTATION_H_</span></div>
<div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160; </div>
<div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;<span class="comment">// common includes</span></div>
<div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;<span class="preprocessor">#include &lt;pcl/pcl_base.h&gt;</span></div>
<div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;<span class="preprocessor">#include &lt;<a class="code" href="common_2include_2pcl_2point__types_8h.html">pcl/point_types.h</a>&gt;</span></div>
<div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;<span class="preprocessor">#include &lt;pcl/point_cloud.h&gt;</span></div>
<div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160; </div>
<div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;<span class="comment">// segmentation and sample consensus includes</span></div>
<div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;<span class="preprocessor">#include &lt;pcl/segmentation/supervoxel_clustering.h&gt;</span></div>
<div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;<span class="preprocessor">#include &lt;pcl/segmentation/lccp_segmentation.h&gt;</span></div>
<div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;<span class="preprocessor">#include &lt;pcl/sample_consensus/sac.h&gt;</span></div>
<div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160; </div>
<div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;<span class="preprocessor">#include &lt;pcl/sample_consensus/sac_model_plane.h&gt;</span></div>
<div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;<span class="preprocessor">#include &lt;pcl/segmentation/extract_clusters.h&gt;</span></div>
<div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160; </div>
<div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;<span class="preprocessor">#define PCL_INSTANTIATE_CPCSegmentation(T) template class PCL_EXPORTS pcl::CPCSegmentation&lt;T&gt;;</span></div>
<div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160; </div>
<div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;<span class="keyword">namespace </span>pcl</div>
<div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;{  </div>
<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt;</div>
<div class="line"><a name="l00068"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_p_c_segmentation.html">   68</a></span>&#160;  <span class="keyword">class </span><a class="code" href="classpcl_1_1_c_p_c_segmentation.html">CPCSegmentation</a> : <span class="keyword">public</span> <a class="code" href="classpcl_1_1_l_c_c_p_segmentation.html">LCCPSegmentation</a>&lt;PointT&gt;</div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;  {</div>
<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;      <span class="keyword">typedef</span> <a class="code" href="structpcl_1_1_point_x_y_z_i_normal.html">PointXYZINormal</a> <a class="code" href="structpcl_1_1_point_x_y_z_i_normal.html">WeightSACPointType</a>;</div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;      <span class="keyword">typedef</span> <a class="code" href="classpcl_1_1_l_c_c_p_segmentation.html">LCCPSegmentation&lt;PointT&gt;</a> <a class="code" href="classpcl_1_1_l_c_c_p_segmentation.html">LCCP</a>;</div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;      <span class="comment">// LCCP typedefs</span></div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;      <span class="keyword">typedef</span> <span class="keyword">typename</span> LCCP::EdgeID EdgeID;</div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;      <span class="keyword">typedef</span> <span class="keyword">typename</span> LCCP::EdgeIterator EdgeIterator;</div>
<div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;      <span class="comment">// LCCP methods</span></div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;      <span class="keyword">using</span> <a class="code" href="classpcl_1_1_l_c_c_p_segmentation.html#a64ade0e74f07da2c8100a1a9d5d46e00">LCCP::calculateConvexConnections</a>;</div>
<div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;      <span class="keyword">using</span> <a class="code" href="classpcl_1_1_l_c_c_p_segmentation.html#ad918a280410d18af75bad10b3134e5ab">LCCP::applyKconvexity</a>;</div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;      <span class="keyword">using</span> <a class="code" href="classpcl_1_1_l_c_c_p_segmentation.html#ad1a810ce20594b9a9309c29f089f0d18">LCCP::doGrouping</a>;</div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;      <span class="keyword">using</span> <a class="code" href="classpcl_1_1_l_c_c_p_segmentation.html#a7f0ada4d9a4918d9dbb9e33e32b23d46">LCCP::mergeSmallSegments</a>;</div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;      <span class="comment">// LCCP variables</span></div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;      <span class="keyword">using</span> <a class="code" href="classpcl_1_1_l_c_c_p_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">LCCP::sv_adjacency_list_</a>;</div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;      <span class="keyword">using</span> <a class="code" href="classpcl_1_1_l_c_c_p_segmentation.html#ab56b15cb177706d688e6773368e123e2">LCCP::k_factor_</a>;</div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;      <span class="keyword">using</span> <a class="code" href="classpcl_1_1_l_c_c_p_segmentation.html#a428e19cb5f6711c7d2e20f31472a876a">LCCP::grouping_data_valid_</a>;</div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;      <span class="keyword">using</span> <a class="code" href="classpcl_1_1_l_c_c_p_segmentation.html#afb6ff37d270e3f16c69c46560a1fafce">LCCP::sv_label_to_seg_label_map_</a>;</div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;      <span class="keyword">using</span> <a class="code" href="classpcl_1_1_l_c_c_p_segmentation.html#a6e8c0fd169543d42903904b02d36239b">LCCP::sv_label_to_supervoxel_map_</a>;</div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;      <span class="keyword">using</span> <a class="code" href="classpcl_1_1_l_c_c_p_segmentation.html#a90f2ad90bee047f31f2c9ad4f3b0c158">LCCP::concavity_tolerance_threshold_</a>;</div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;      <span class="keyword">using</span> <a class="code" href="classpcl_1_1_l_c_c_p_segmentation.html#aa9f7011e99af9d3849937ff5370c2e11">LCCP::seed_resolution_</a>;</div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;      <span class="keyword">using</span> <a class="code" href="classpcl_1_1_l_c_c_p_segmentation.html#a0ebcf3b12da8ec8ff9029a4bc77292b6">LCCP::supervoxels_set_</a>;</div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160; </div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;    <span class="keyword">public</span>:</div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;      <a class="code" href="classpcl_1_1_c_p_c_segmentation.html">CPCSegmentation</a> ();</div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;      <span class="keyword">virtual</span></div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;      ~<a class="code" href="classpcl_1_1_c_p_c_segmentation.html">CPCSegmentation</a> ();</div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160; </div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;      <span class="keywordtype">void</span></div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;      <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a0ff7ee11473d36cbb774f90de8064908">segment</a> ();</div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160; </div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;      <span class="keyword">inline</span> <span class="keywordtype">void</span></div>
<div class="line"><a name="l00109"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_p_c_segmentation.html#a0fdcebc606820bc008e779230503da04">  109</a></span>&#160;      <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a0fdcebc606820bc008e779230503da04">setCutting</a> (<span class="keyword">const</span> uint32_t max_cuts = 20,</div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;                  <span class="keyword">const</span> uint32_t cutting_min_segments = 0,</div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;                  <span class="keyword">const</span> <span class="keywordtype">float</span> cutting_min_score = 0.16,</div>
<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;                  <span class="keyword">const</span> <span class="keywordtype">bool</span> locally_constrained = <span class="keyword">true</span>,</div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;                  <span class="keyword">const</span> <span class="keywordtype">bool</span> directed_cutting = <span class="keyword">true</span>,</div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;                  <span class="keyword">const</span> <span class="keywordtype">bool</span> clean_cutting = <span class="keyword">false</span>)</div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;      {</div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;        <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a6293e15b7d22fbb19d7819bedba683f1">max_cuts_</a> = max_cuts;</div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;        <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a6eda4c6ab0c6d0f55b11c5a666accd7f">min_segment_size_for_cutting_</a> = cutting_min_segments;</div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;        <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#ac04198491da197fdbb9478dbf682f1ec">min_cut_score_</a> = cutting_min_score;</div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;        <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a6016df56b09c1f9b13db954e1805d930">use_local_constrains_</a> = locally_constrained;</div>
<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;        <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#aceaa9f8130856b9304830674ac7515c8">use_directed_weights_</a> = directed_cutting;</div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;        <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a743b8b7bfcb33d9edfcbd9fa1ecc2eac">use_clean_cutting_</a> = clean_cutting;</div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;      }</div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160; </div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;      <span class="keyword">inline</span> <span class="keywordtype">void</span> </div>
<div class="line"><a name="l00127"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_p_c_segmentation.html#ae45407cfb0975dc3ab6bb1f77c6df512">  127</a></span>&#160;      <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#ae45407cfb0975dc3ab6bb1f77c6df512">setRANSACIterations</a> (<span class="keyword">const</span> uint32_t ransac_iterations)</div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;      {</div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;        <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#aa1eac80686a308fdd04592bdefd9e6bd">ransac_itrs_</a> = ransac_iterations;</div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;      }</div>
<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160; </div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;    <span class="keyword">private</span>:      </div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160; </div>
<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;      <span class="keywordtype">void</span></div>
<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;      <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#aba7a4f7d9481b0c9c88edc6d301964d9">applyCuttingPlane</a> (uint32_t depth_levels_left);</div>
<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160; </div>
<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160; </div>
<div class="line"><a name="l00143"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_p_c_segmentation.html#a6293e15b7d22fbb19d7819bedba683f1">  143</a></span>&#160;      uint32_t <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a6293e15b7d22fbb19d7819bedba683f1">max_cuts_</a>;</div>
<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160; </div>
<div class="line"><a name="l00146"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_p_c_segmentation.html#a6eda4c6ab0c6d0f55b11c5a666accd7f">  146</a></span>&#160;      uint32_t <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a6eda4c6ab0c6d0f55b11c5a666accd7f">min_segment_size_for_cutting_</a>;</div>
<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160; </div>
<div class="line"><a name="l00149"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_p_c_segmentation.html#ac04198491da197fdbb9478dbf682f1ec">  149</a></span>&#160;      <span class="keywordtype">float</span> <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#ac04198491da197fdbb9478dbf682f1ec">min_cut_score_</a>;</div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160; </div>
<div class="line"><a name="l00152"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_p_c_segmentation.html#a6016df56b09c1f9b13db954e1805d930">  152</a></span>&#160;      <span class="keywordtype">bool</span> <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a6016df56b09c1f9b13db954e1805d930">use_local_constrains_</a>;</div>
<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160; </div>
<div class="line"><a name="l00155"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_p_c_segmentation.html#aceaa9f8130856b9304830674ac7515c8">  155</a></span>&#160;      <span class="keywordtype">bool</span> <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#aceaa9f8130856b9304830674ac7515c8">use_directed_weights_</a>;</div>
<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160; </div>
<div class="line"><a name="l00158"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_p_c_segmentation.html#a743b8b7bfcb33d9edfcbd9fa1ecc2eac">  158</a></span>&#160;      <span class="keywordtype">bool</span> <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#a743b8b7bfcb33d9edfcbd9fa1ecc2eac">use_clean_cutting_</a>;</div>
<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;      </div>
<div class="line"><a name="l00161"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_p_c_segmentation.html#aa1eac80686a308fdd04592bdefd9e6bd">  161</a></span>&#160;      uint32_t <a class="code" href="classpcl_1_1_c_p_c_segmentation.html#aa1eac80686a308fdd04592bdefd9e6bd">ransac_itrs_</a>;</div>
<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;     </div>
<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;      </div>
<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;<span class="comment">/******************************************* Directional weighted RANSAC declarations ******************************************************************/</span>      </div>
<div class="line"><a name="l00176"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html">  176</a></span>&#160;      <span class="keyword">class </span><a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html">WeightedRandomSampleConsensus</a> : <span class="keyword">public</span> <a class="code" href="classpcl_1_1_sample_consensus.html">SampleConsensus</a>&lt;WeightSACPointType&gt;</div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;      {</div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;          <span class="keyword">typedef</span> <span class="keyword">typename</span> SampleConsensusModel&lt;WeightSACPointType&gt;::Ptr SampleConsensusModelPtr;</div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160; </div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;        <span class="keyword">public</span>:</div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;          <span class="keyword">typedef</span> boost::shared_ptr&lt;WeightedRandomSampleConsensus&gt; Ptr;</div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;          <span class="keyword">typedef</span> boost::shared_ptr&lt;const WeightedRandomSampleConsensus&gt; ConstPtr;</div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160; </div>
<div class="line"><a name="l00188"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a49f312ddf01f8ffee1efeb3374b2eef7">  188</a></span>&#160;          <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a49f312ddf01f8ffee1efeb3374b2eef7">WeightedRandomSampleConsensus</a> (<span class="keyword">const</span> SampleConsensusModelPtr &amp;model, </div>
<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;                                        <span class="keywordtype">bool</span> random = <span class="keyword">false</span>)</div>
<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;            : <a class="code" href="classpcl_1_1_sample_consensus.html">SampleConsensus</a>&lt;<a class="code" href="structpcl_1_1_point_x_y_z_i_normal.html">WeightSACPointType</a>&gt; (model, random)</div>
<div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;          {</div>
<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;            <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a617804fa3f1e32afe3f755d54e03ee98">initialize</a> ();</div>
<div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;          }</div>
<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160; </div>
<div class="line"><a name="l00200"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a724b2439427951d9e96688341e263761">  200</a></span>&#160;          <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a724b2439427951d9e96688341e263761">WeightedRandomSampleConsensus</a> (<span class="keyword">const</span> SampleConsensusModelPtr &amp;model,</div>
<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;                                        <span class="keywordtype">double</span> threshold,</div>
<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;                                        <span class="keywordtype">bool</span> random = <span class="keyword">false</span>)</div>
<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;            : <a class="code" href="classpcl_1_1_sample_consensus.html">SampleConsensus</a>&lt;<a class="code" href="structpcl_1_1_point_x_y_z_i_normal.html">WeightSACPointType</a>&gt; (model, threshold, random)</div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;          {</div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;            <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a617804fa3f1e32afe3f755d54e03ee98">initialize</a> ();</div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;          }</div>
<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160; </div>
<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;          <span class="keywordtype">bool</span></div>
<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;          <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#aaa2dc352bd71e275a23de67ac522974f">computeModel</a> (<span class="keywordtype">int</span> debug_verbosity_level = 0);</div>
<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160; </div>
<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;          <span class="keywordtype">void</span></div>
<div class="line"><a name="l00218"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a6313e39510545b961cfbed0373b7bbde">  218</a></span>&#160;          <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a6313e39510545b961cfbed0373b7bbde">setWeights</a> (<span class="keyword">const</span> std::vector&lt;double&gt; &amp;weights,</div>
<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;                      <span class="keyword">const</span> <span class="keywordtype">bool</span> directed_weights = <span class="keyword">false</span>)</div>
<div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;          {</div>
<div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;            <span class="keywordflow">if</span> (weights.size () != <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#ac8f5af30b240aa1d7c21082ef2f84ed7">full_cloud_pt_indices_</a>-&gt;size ())</div>
<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;            {</div>
<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;              PCL_ERROR (<span class="stringliteral">&quot;[pcl::WeightedRandomSampleConsensus::setWeights] Cannot assign weights. Weight vector needs to have the same length as the input pointcloud\n&quot;</span>);</div>
<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;              <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;            }</div>
<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;            <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a5f272fa6787532bbe7a7f14ef40f79d6">weights_</a> = weights;</div>
<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;            <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a690b982ca4e9efbc8cc8bfd1954db4dc">model_pt_indices_</a>-&gt;clear ();</div>
<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;            <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; weights.size (); ++i)</div>
<div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;            {</div>
<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;              <span class="keywordflow">if</span> (weights[i] &gt; std::numeric_limits&lt;double&gt;::epsilon ())</div>
<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;                <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a690b982ca4e9efbc8cc8bfd1954db4dc">model_pt_indices_</a>-&gt;push_back (i);</div>
<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;            }</div>
<div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;            <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#afdf74f8e57d514108d59d16829b5b446">use_directed_weights_</a> = directed_weights;</div>
<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;          }</div>
<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160; </div>
<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;          <span class="keywordtype">double</span></div>
<div class="line"><a name="l00240"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a5a291239e9d29732e29919adc43f7ace">  240</a></span>&#160;          <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a5a291239e9d29732e29919adc43f7ace">getBestScore</a> ()<span class="keyword"> const</span></div>
<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;<span class="keyword">          </span>{</div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;            <span class="keywordflow">return</span> (<a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#aff0d5c9c04a5d9dee5b66f44c04303ec">best_score_</a>);</div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;          }</div>
<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160; </div>
<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;        <span class="keyword">protected</span>:</div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;          <span class="keywordtype">void</span></div>
<div class="line"><a name="l00248"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a617804fa3f1e32afe3f755d54e03ee98">  248</a></span>&#160;          <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a617804fa3f1e32afe3f755d54e03ee98">initialize</a> ()</div>
<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;          {</div>
<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;            <span class="comment">// Maximum number of trials before we give up.</span></div>
<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;            <a class="code" href="classpcl_1_1_sample_consensus.html#ab5ca8dbf21b2a1c6ed9c1e8d3eba853c">max_iterations_</a> = 10000;</div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;            <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#afdf74f8e57d514108d59d16829b5b446">use_directed_weights_</a> = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;            <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a690b982ca4e9efbc8cc8bfd1954db4dc">model_pt_indices_</a> = boost::shared_ptr&lt;std::vector&lt;int&gt; &gt; (<span class="keyword">new</span> std::vector&lt;int&gt; ());</div>
<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;            <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#ac8f5af30b240aa1d7c21082ef2f84ed7">full_cloud_pt_indices_</a>.reset (<span class="keyword">new</span> std::vector&lt;int&gt; (* (<a class="code" href="classpcl_1_1_sample_consensus.html#aa4953d080c1ab4223cde8ff8d8cabc52">sac_model_</a>-&gt;getIndices ())));       </div>
<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;            <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#ab06480ee4efa1545f1fbf84ff58a5eca">point_cloud_ptr_</a> = <a class="code" href="classpcl_1_1_sample_consensus.html#aa4953d080c1ab4223cde8ff8d8cabc52">sac_model_</a>-&gt;getInputCloud ();</div>
<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;          }</div>
<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;          </div>
<div class="line"><a name="l00259"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#afdf74f8e57d514108d59d16829b5b446">  259</a></span>&#160;          <span class="keywordtype">bool</span> <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#afdf74f8e57d514108d59d16829b5b446">use_directed_weights_</a>;</div>
<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;          </div>
<div class="line"><a name="l00262"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a5f272fa6787532bbe7a7f14ef40f79d6">  262</a></span>&#160;          std::vector&lt;double&gt; <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a5f272fa6787532bbe7a7f14ef40f79d6">weights_</a>;</div>
<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;          </div>
<div class="line"><a name="l00265"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a690b982ca4e9efbc8cc8bfd1954db4dc">  265</a></span>&#160;          boost::shared_ptr&lt;std::vector&lt;int&gt; &gt; <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a690b982ca4e9efbc8cc8bfd1954db4dc">model_pt_indices_</a>;</div>
<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;          </div>
<div class="line"><a name="l00268"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#ac8f5af30b240aa1d7c21082ef2f84ed7">  268</a></span>&#160;          boost::shared_ptr&lt;std::vector&lt;int&gt; &gt; <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#ac8f5af30b240aa1d7c21082ef2f84ed7">full_cloud_pt_indices_</a>;</div>
<div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;          </div>
<div class="line"><a name="l00271"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#ab06480ee4efa1545f1fbf84ff58a5eca">  271</a></span>&#160;          boost::shared_ptr&lt;const pcl::PointCloud&lt;WeightSACPointType&gt; &gt; <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#ab06480ee4efa1545f1fbf84ff58a5eca">point_cloud_ptr_</a>;</div>
<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;          </div>
<div class="line"><a name="l00274"></a><span class="lineno"><a class="line" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#aff0d5c9c04a5d9dee5b66f44c04303ec">  274</a></span>&#160;          <span class="keywordtype">double</span> <a class="code" href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#aff0d5c9c04a5d9dee5b66f44c04303ec">best_score_</a>;</div>
<div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;      };</div>
<div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;      </div>
<div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;  };</div>
<div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;}</div>
<div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160; </div>
<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;<span class="preprocessor">#ifdef PCL_NO_PRECOMPILE</span></div>
<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;<span class="preprocessor">  #include &lt;pcl/segmentation/impl/cpc_segmentation.hpp&gt;</span></div>
<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;<span class="preprocessor">#elif defined(PCL_ONLY_CORE_POINT_TYPES)</span></div>
<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;  <span class="comment">//pcl::PointXYZINormal is not a core point type (so we cannot use the precompiled classes here)</span></div>
<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;<span class="preprocessor">  #include &lt;pcl/sample_consensus/impl/sac_model_plane.hpp&gt;</span></div>
<div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;<span class="preprocessor">  #include &lt;pcl/segmentation/impl/extract_clusters.hpp&gt;</span>  </div>
<div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;<span class="preprocessor">#endif </span><span class="comment">// PCL_NO_PRECOMPILE / PCL_ONLY_CORE_POINT_TYPES</span></div>
<div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160; </div>
<div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;<span class="preprocessor">#endif </span><span class="comment">// PCL_SEGMENTATION_CPC_SEGMENTATION_H_</span></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus_html"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html">pcl::CPCSegmentation::WeightedRandomSampleConsensus</a></div><div class="ttdoc">WeightedRandomSampleConsensus represents an implementation of the Directionally Weighted RANSAC algor...</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:177</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus_html_a49f312ddf01f8ffee1efeb3374b2eef7"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a49f312ddf01f8ffee1efeb3374b2eef7">pcl::CPCSegmentation::WeightedRandomSampleConsensus::WeightedRandomSampleConsensus</a></div><div class="ttdeci">WeightedRandomSampleConsensus(const SampleConsensusModelPtr &amp;model, bool random=false)</div><div class="ttdoc">WeightedRandomSampleConsensus (Weighted RAndom SAmple Consensus) main constructor</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:188</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus_html_a5a291239e9d29732e29919adc43f7ace"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a5a291239e9d29732e29919adc43f7ace">pcl::CPCSegmentation::WeightedRandomSampleConsensus::getBestScore</a></div><div class="ttdeci">double getBestScore() const</div><div class="ttdoc">Get the best score</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:240</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus_html_a5f272fa6787532bbe7a7f14ef40f79d6"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a5f272fa6787532bbe7a7f14ef40f79d6">pcl::CPCSegmentation::WeightedRandomSampleConsensus::weights_</a></div><div class="ttdeci">std::vector&lt; double &gt; weights_</div><div class="ttdoc">vector of weights assigned to points. Set by the setWeights-method</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:262</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus_html_a617804fa3f1e32afe3f755d54e03ee98"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a617804fa3f1e32afe3f755d54e03ee98">pcl::CPCSegmentation::WeightedRandomSampleConsensus::initialize</a></div><div class="ttdeci">void initialize()</div><div class="ttdoc">Initialize the model parameters. Called by the constructors.</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:248</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus_html_a6313e39510545b961cfbed0373b7bbde"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a6313e39510545b961cfbed0373b7bbde">pcl::CPCSegmentation::WeightedRandomSampleConsensus::setWeights</a></div><div class="ttdeci">void setWeights(const std::vector&lt; double &gt; &amp;weights, const bool directed_weights=false)</div><div class="ttdoc">Set the weights for the input points</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:218</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus_html_a690b982ca4e9efbc8cc8bfd1954db4dc"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a690b982ca4e9efbc8cc8bfd1954db4dc">pcl::CPCSegmentation::WeightedRandomSampleConsensus::model_pt_indices_</a></div><div class="ttdeci">boost::shared_ptr&lt; std::vector&lt; int &gt; &gt; model_pt_indices_</div><div class="ttdoc">The indices used for estimating the RANSAC model. Only those whose weight is &gt; 0</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:265</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus_html_a724b2439427951d9e96688341e263761"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#a724b2439427951d9e96688341e263761">pcl::CPCSegmentation::WeightedRandomSampleConsensus::WeightedRandomSampleConsensus</a></div><div class="ttdeci">WeightedRandomSampleConsensus(const SampleConsensusModelPtr &amp;model, double threshold, bool random=false)</div><div class="ttdoc">WeightedRandomSampleConsensus (Weighted RAndom SAmple Consensus) main constructor</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:200</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus_html_aaa2dc352bd71e275a23de67ac522974f"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#aaa2dc352bd71e275a23de67ac522974f">pcl::CPCSegmentation::WeightedRandomSampleConsensus::computeModel</a></div><div class="ttdeci">bool computeModel(int debug_verbosity_level=0)</div><div class="ttdoc">Compute the actual model and find the inliers</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.hpp:290</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus_html_ab06480ee4efa1545f1fbf84ff58a5eca"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#ab06480ee4efa1545f1fbf84ff58a5eca">pcl::CPCSegmentation::WeightedRandomSampleConsensus::point_cloud_ptr_</a></div><div class="ttdeci">boost::shared_ptr&lt; const pcl::PointCloud&lt; WeightSACPointType &gt; &gt; point_cloud_ptr_</div><div class="ttdoc">Pointer to the input PointCloud</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:271</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus_html_ac8f5af30b240aa1d7c21082ef2f84ed7"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#ac8f5af30b240aa1d7c21082ef2f84ed7">pcl::CPCSegmentation::WeightedRandomSampleConsensus::full_cloud_pt_indices_</a></div><div class="ttdeci">boost::shared_ptr&lt; std::vector&lt; int &gt; &gt; full_cloud_pt_indices_</div><div class="ttdoc">The complete list of indices used for the model evaluation</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:268</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus_html_afdf74f8e57d514108d59d16829b5b446"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#afdf74f8e57d514108d59d16829b5b446">pcl::CPCSegmentation::WeightedRandomSampleConsensus::use_directed_weights_</a></div><div class="ttdeci">bool use_directed_weights_</div><div class="ttdoc">weight each positive weight point by the inner product between the normal and the plane normal</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:259</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus_html_aff0d5c9c04a5d9dee5b66f44c04303ec"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation_1_1_weighted_random_sample_consensus.html#aff0d5c9c04a5d9dee5b66f44c04303ec">pcl::CPCSegmentation::WeightedRandomSampleConsensus::best_score_</a></div><div class="ttdeci">double best_score_</div><div class="ttdoc">Highest score found so far</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:274</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html">pcl::CPCSegmentation</a></div><div class="ttdoc">A segmentation algorithm partitioning a supervoxel graph. It uses planar cuts induced by local concav...</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:69</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_a0fdcebc606820bc008e779230503da04"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#a0fdcebc606820bc008e779230503da04">pcl::CPCSegmentation::setCutting</a></div><div class="ttdeci">void setCutting(const uint32_t max_cuts=20, const uint32_t cutting_min_segments=0, const float cutting_min_score=0.16, const bool locally_constrained=true, const bool directed_cutting=true, const bool clean_cutting=false)</div><div class="ttdoc">Determines if we want to use cutting planes</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:109</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_a0ff7ee11473d36cbb774f90de8064908"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#a0ff7ee11473d36cbb774f90de8064908">pcl::CPCSegmentation::segment</a></div><div class="ttdeci">void segment()</div><div class="ttdoc">Merge supervoxels using cuts through local convexities. The input parameters are generated by using t...</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.hpp:60</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_a6016df56b09c1f9b13db954e1805d930"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#a6016df56b09c1f9b13db954e1805d930">pcl::CPCSegmentation::use_local_constrains_</a></div><div class="ttdeci">bool use_local_constrains_</div><div class="ttdoc">Use local constrains for cutting</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:152</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_a6293e15b7d22fbb19d7819bedba683f1"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#a6293e15b7d22fbb19d7819bedba683f1">pcl::CPCSegmentation::max_cuts_</a></div><div class="ttdeci">uint32_t max_cuts_</div><div class="ttdoc">*** Parameters *** ///</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:143</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_a6eda4c6ab0c6d0f55b11c5a666accd7f"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#a6eda4c6ab0c6d0f55b11c5a666accd7f">pcl::CPCSegmentation::min_segment_size_for_cutting_</a></div><div class="ttdeci">uint32_t min_segment_size_for_cutting_</div><div class="ttdoc">Minimum segment size for cutting</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:146</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_a743b8b7bfcb33d9edfcbd9fa1ecc2eac"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#a743b8b7bfcb33d9edfcbd9fa1ecc2eac">pcl::CPCSegmentation::use_clean_cutting_</a></div><div class="ttdeci">bool use_clean_cutting_</div><div class="ttdoc">Use clean cutting</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:158</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_aa1eac80686a308fdd04592bdefd9e6bd"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#aa1eac80686a308fdd04592bdefd9e6bd">pcl::CPCSegmentation::ransac_itrs_</a></div><div class="ttdeci">uint32_t ransac_itrs_</div><div class="ttdoc">Interations for RANSAC</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:161</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_aba7a4f7d9481b0c9c88edc6d301964d9"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#aba7a4f7d9481b0c9c88edc6d301964d9">pcl::CPCSegmentation::applyCuttingPlane</a></div><div class="ttdeci">void applyCuttingPlane(uint32_t depth_levels_left)</div><div class="ttdoc">Used in for CPC to find and fit cutting planes to the pointcloud.</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.hpp:86</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_ac04198491da197fdbb9478dbf682f1ec"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#ac04198491da197fdbb9478dbf682f1ec">pcl::CPCSegmentation::min_cut_score_</a></div><div class="ttdeci">float min_cut_score_</div><div class="ttdoc">Cut_score threshold</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:149</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_aceaa9f8130856b9304830674ac7515c8"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#aceaa9f8130856b9304830674ac7515c8">pcl::CPCSegmentation::use_directed_weights_</a></div><div class="ttdeci">bool use_directed_weights_</div><div class="ttdoc">Use directed weights for the cutting</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:155</div></div>
<div class="ttc" id="aclasspcl_1_1_c_p_c_segmentation_html_ae45407cfb0975dc3ab6bb1f77c6df512"><div class="ttname"><a href="classpcl_1_1_c_p_c_segmentation.html#ae45407cfb0975dc3ab6bb1f77c6df512">pcl::CPCSegmentation::setRANSACIterations</a></div><div class="ttdeci">void setRANSACIterations(const uint32_t ransac_iterations)</div><div class="ttdoc">Set the number of iterations for the weighted RANSAC step (best cut estimations)</div><div class="ttdef"><b>Definition:</b> cpc_segmentation.h:127</div></div>
<div class="ttc" id="aclasspcl_1_1_l_c_c_p_segmentation_html"><div class="ttname"><a href="classpcl_1_1_l_c_c_p_segmentation.html">pcl::LCCPSegmentation</a></div><div class="ttdoc">A simple segmentation algorithm partitioning a supervoxel graph into groups of locally convex connect...</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.h:60</div></div>
<div class="ttc" id="aclasspcl_1_1_l_c_c_p_segmentation_html_a0ebcf3b12da8ec8ff9029a4bc77292b6"><div class="ttname"><a href="classpcl_1_1_l_c_c_p_segmentation.html#a0ebcf3b12da8ec8ff9029a4bc77292b6">pcl::LCCPSegmentation::supervoxels_set_</a></div><div class="ttdeci">bool supervoxels_set_</div><div class="ttdoc">Marks if supervoxels have been set by calling setInputSupervoxels</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.h:308</div></div>
<div class="ttc" id="aclasspcl_1_1_l_c_c_p_segmentation_html_a428e19cb5f6711c7d2e20f31472a876a"><div class="ttname"><a href="classpcl_1_1_l_c_c_p_segmentation.html#a428e19cb5f6711c7d2e20f31472a876a">pcl::LCCPSegmentation::grouping_data_valid_</a></div><div class="ttdeci">bool grouping_data_valid_</div><div class="ttdoc">Marks if valid grouping data (sv_adjacency_list_, sv_label_to_seg_label_map_, processed_) is avaiable</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.h:305</div></div>
<div class="ttc" id="aclasspcl_1_1_l_c_c_p_segmentation_html_a64ade0e74f07da2c8100a1a9d5d46e00"><div class="ttname"><a href="classpcl_1_1_l_c_c_p_segmentation.html#a64ade0e74f07da2c8100a1a9d5d46e00">pcl::LCCPSegmentation::calculateConvexConnections</a></div><div class="ttdeci">void calculateConvexConnections(SupervoxelAdjacencyList &amp;adjacency_list_arg)</div><div class="ttdoc">Calculates convexity of edges and saves this to the adjacency graph.</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.hpp:437</div></div>
<div class="ttc" id="aclasspcl_1_1_l_c_c_p_segmentation_html_a6e8c0fd169543d42903904b02d36239b"><div class="ttname"><a href="classpcl_1_1_l_c_c_p_segmentation.html#a6e8c0fd169543d42903904b02d36239b">pcl::LCCPSegmentation::sv_label_to_supervoxel_map_</a></div><div class="ttdeci">std::map&lt; uint32_t, typename pcl::Supervoxel&lt; PointT &gt;::Ptr &gt; sv_label_to_supervoxel_map_</div><div class="ttdoc">map from the supervoxel labels to the supervoxel objects</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.h:339</div></div>
<div class="ttc" id="aclasspcl_1_1_l_c_c_p_segmentation_html_a7f0ada4d9a4918d9dbb9e33e32b23d46"><div class="ttname"><a href="classpcl_1_1_l_c_c_p_segmentation.html#a7f0ada4d9a4918d9dbb9e33e32b23d46">pcl::LCCPSegmentation::mergeSmallSegments</a></div><div class="ttdeci">void mergeSmallSegments()</div><div class="ttdoc">Segments smaller than min_segment_size_ are merged to the label of largest neighbor</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.hpp:171</div></div>
<div class="ttc" id="aclasspcl_1_1_l_c_c_p_segmentation_html_a90f2ad90bee047f31f2c9ad4f3b0c158"><div class="ttname"><a href="classpcl_1_1_l_c_c_p_segmentation.html#a90f2ad90bee047f31f2c9ad4f3b0c158">pcl::LCCPSegmentation::concavity_tolerance_threshold_</a></div><div class="ttdeci">float concavity_tolerance_threshold_</div><div class="ttdoc">*** Parameters *** ///</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.h:302</div></div>
<div class="ttc" id="aclasspcl_1_1_l_c_c_p_segmentation_html_aa9f7011e99af9d3849937ff5370c2e11"><div class="ttname"><a href="classpcl_1_1_l_c_c_p_segmentation.html#aa9f7011e99af9d3849937ff5370c2e11">pcl::LCCPSegmentation::seed_resolution_</a></div><div class="ttdeci">float seed_resolution_</div><div class="ttdoc">Seed resolution of the supervoxels (used only for smoothness check)</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.h:320</div></div>
<div class="ttc" id="aclasspcl_1_1_l_c_c_p_segmentation_html_ab56b15cb177706d688e6773368e123e2"><div class="ttname"><a href="classpcl_1_1_l_c_c_p_segmentation.html#ab56b15cb177706d688e6773368e123e2">pcl::LCCPSegmentation::k_factor_</a></div><div class="ttdeci">uint32_t k_factor_</div><div class="ttdoc">Factor used for k-convexity</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.h:326</div></div>
<div class="ttc" id="aclasspcl_1_1_l_c_c_p_segmentation_html_ad1a810ce20594b9a9309c29f089f0d18"><div class="ttname"><a href="classpcl_1_1_l_c_c_p_segmentation.html#ad1a810ce20594b9a9309c29f089f0d18">pcl::LCCPSegmentation::doGrouping</a></div><div class="ttdeci">void doGrouping()</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.hpp:308</div></div>
<div class="ttc" id="aclasspcl_1_1_l_c_c_p_segmentation_html_ad918a280410d18af75bad10b3134e5ab"><div class="ttname"><a href="classpcl_1_1_l_c_c_p_segmentation.html#ad918a280410d18af75bad10b3134e5ab">pcl::LCCPSegmentation::applyKconvexity</a></div><div class="ttdeci">void applyKconvexity(const unsigned int k_arg)</div><div class="ttdoc">Connections are only convex if this is true for at least k_arg common neighbors of the two patches....</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.hpp:371</div></div>
<div class="ttc" id="aclasspcl_1_1_l_c_c_p_segmentation_html_adff367bb7eab2ec652da194f36ad2ab4"><div class="ttname"><a href="classpcl_1_1_l_c_c_p_segmentation.html#adff367bb7eab2ec652da194f36ad2ab4">pcl::LCCPSegmentation::sv_adjacency_list_</a></div><div class="ttdeci">SupervoxelAdjacencyList sv_adjacency_list_</div><div class="ttdoc">Adjacency graph with the supervoxel labels as nodes and edges between adjacent supervoxels</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.h:336</div></div>
<div class="ttc" id="aclasspcl_1_1_l_c_c_p_segmentation_html_afb6ff37d270e3f16c69c46560a1fafce"><div class="ttname"><a href="classpcl_1_1_l_c_c_p_segmentation.html#afb6ff37d270e3f16c69c46560a1fafce">pcl::LCCPSegmentation::sv_label_to_seg_label_map_</a></div><div class="ttdeci">std::map&lt; uint32_t, uint32_t &gt; sv_label_to_seg_label_map_</div><div class="ttdoc">Storing relation between original SuperVoxel Labels and new segmantion labels.</div><div class="ttdef"><b>Definition:</b> lccp_segmentation.h:343</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html">pcl::SampleConsensus</a></div><div class="ttdoc">SampleConsensus represents the base class. All sample consensus methods must inherit from this class.</div><div class="ttdef"><b>Definition:</b> sac.h:57</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_aa4953d080c1ab4223cde8ff8d8cabc52"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#aa4953d080c1ab4223cde8ff8d8cabc52">pcl::SampleConsensus&lt; WeightSACPointType &gt;::sac_model_</a></div><div class="ttdeci">SampleConsensusModelPtr sac_model_</div><div class="ttdoc">The underlying data model used (i.e. what is it that we attempt to search for).</div><div class="ttdef"><b>Definition:</b> sac.h:310</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_ab5ca8dbf21b2a1c6ed9c1e8d3eba853c"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#ab5ca8dbf21b2a1c6ed9c1e8d3eba853c">pcl::SampleConsensus&lt; WeightSACPointType &gt;::max_iterations_</a></div><div class="ttdeci">int max_iterations_</div><div class="ttdoc">Maximum number of iterations before giving up.</div><div class="ttdef"><b>Definition:</b> sac.h:331</div></div>
<div class="ttc" id="acommon_2include_2pcl_2point__types_8h_html"><div class="ttname"><a href="common_2include_2pcl_2point__types_8h.html">point_types.h</a></div></div>
<div class="ttc" id="astructpcl_1_1_point_x_y_z_i_normal_html"><div class="ttname"><a href="structpcl_1_1_point_x_y_z_i_normal.html">pcl::PointXYZINormal</a></div><div class="ttdoc">A point structure representing Euclidean xyz coordinates, intensity, together with normal coordinates...</div><div class="ttdef"><b>Definition:</b> point_types.hpp:969</div></div>
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